Digital communication of information transmitted by one or more transmitters and received by one or more receivers generally requires special signalling methods. One such example is orthogonal frequency-division multiplexing, known as OFDM, that uses frequency-division multiplexed signals to carry data, which are conveyed in channels.
To mitigate against the effects of noise and fading within the channels used to convey the signals, forward error correction techniques are generally employed, together with frequency and/or time interleaving of the signal to provide redundancy and to increase the robustness of the signals as will be explained below.
The quality of a channel used to convey the signal can vary, particularly with wireless communication protocols and channels, due to multipath effects caused by diffraction or reflection of signals from obstructions such as buildings in the signals path. For wireless signals, depending on whether reflected signals add up coherently or out of phase, a received signal may be in a good channel where the signal is a good representative of the sent signal, or in a deep fade channel where the signal is attenuated, and/or phase shifted when travelling from the source to the receiver. To overcome the negative effects of poor deep fade channels, wireless systems are designed to insert redundancies in the transmitted signal so that faded signals can be reconstructed at the receiver side. As noted above, this is done by the use of channel coding such as convolutional coding, turbo coding or low density parity check (LDPC) codes etc.
Typically, when a signal is sent, first the bits are encoded with a channel coder, e.g., a FEC (Forward Error Correction) block or encoder and redundancy bits are inserted. Next these bits are interleaved by an interleaver block over a different time and frequency of the signal so that they experience different channels in the coded block. The bits are then modulated and sent over the, for example, wireless channel. At the receiver side, the transmitted bits are demodulated and the reliability information of each bit is computed in the form of a probability of likelihood that the value of the received bit is (for example, for a digital signal) a particular value. This information can be considered to be soft bits. One example of such soft bits are log-likelihood-ratios (LLRs) which, depending upon their value, provide a likelihood that the received bit has one value or a different value. In wireless communication examples, the LLRs can be used by the receiver to determine the likelihood that the value of a received bit is either a 0 or a 1 (for a digital signal). The LLRs can have a different resolution depending on, for example, the application and/or the wireless system. A deinterleaver block can then align the LLRs against the respective received bit, compensating (or inverting) the operation of the interleaver at transmitter side, and feeding the compensated value of the received bit to the FEC decoder to be processed.
Channel conditions can vary depending upon the positions of the transmitter and receiver and also due to variations in the obstructions in the line of signal path. For example, the channels of signals received by a moving car can vary depending upon the position of the car relative to its surroundings. Such variations are difficult to predict and can be considered random. Accordingly, each channel can be a random varying channel with varying channel conditions. Due to this potential varying nature of channel conditions, to make channel coding more robust against fading effects, the encoded bits are interleaved such that the bits that in that instance are experiencing similar channel conditions, e.g., bits that are experiencing good channels or faded channels, are spread all over different parts of the channel code so that they can be corrected in a more efficient way. Thus, interleaving plays an important role in wireless communication systems. As the size of the interleaver block is increased, the system becomes more robust against fading effects.
To overcome the fading effects of the wireless channel, the digital audio broadcasting (DAB) standard chose the interleaver size as 384 ms. Thus, the deinterleaver block needs to store the LLRs within a memory such as flash memory during the 384 ms duration so that it can feed the LLRs to the FEC decoder correctly. Every standard has its own design choice of interleaving size. As the size of the interleaver is increased, the number of softbits to be stored increases, leading to larger memory cost if the bits need be stored on the receiver.
The interleaver/deinterleaver memory generally has a large cost of implementation within an integrated circuit area used for a wireless receiver. Accordingly, if a large interleaving size is chosen, this choice increases the cost of the receiver. One conventional way of reducing the memory cost requirement of the interleaver/deinterleaver memory (i.e. the size of the flash memory or random access memory or other form of memory) is to reduce the number of bits used to represent the LLRs. However, as the number of bits used to represent the LLRs is decreased, the FEC decoder works less effectively, correcting less erroneous bits which decreases the reliability of reception. Thus, for example in a DAB receiver, the listener hears lower quality audio signal or in a digital video broadcasting—e.g. a second generation (DVB-T2) receiver, the viewer experiences more video frame errors.
In a conventional receiver, the LLRs are generated as words stored at an address in memory of the receiver. The words have a word width measured in N bits. Accordingly, the words of the LLRs are stored and read in N bits, deinterleaved, and fed to the FEC decoder still in an N bit accuracy. Thus, the accuracy of the LLRs does not change until it is used by a FEC decoder.
As an example of another modulation protocol, bit interleaved coded modulation (BICM) is widely used in wireless systems such as digital radio, TV broadcast, WiFi, cellular and satellite systems as well as wired systems to overcome the negative effects of noise and fading in the wireless/wired channel. The bits are encoded, interleaved and mapped to symbols at the transmitter side and demodulation, deinterleaving and decoding is done at the receiver side. To achieve higher data rates, larger constellation sizes are used, e.g., 64 quadrature amplitude modulation (QAM) in ISDB-T/IEEE 802.11a/g, 4096 QAM in DVB-C2.
With the use of larger constellation sizes such as 64 QAM and up, the required memory/speed to store/transfer the LLRs per symbol multiplies with the number of bits transmitted per symbol, e.g., a 6 times memory or bandwidth requirement for 64QAM than for binary phase shift keying (BPSK) modulation.
In a conventional distributed receiver system, distributed receivers compute the LLRs of each bit and send this information to the master processing unit that performs de-interleaving and FEC decoding. In this case, each receiver requires a data link to the master processing unit with minimum data rate of (N×T×1/r) where N is the resolution of the LLRs, T is the net throughput of the data intended to be decoded and r is the FEC coding rate.
Although a distributed reception system does not necessarily have the same issues with the cost of memory of storing LLR values, each computed LLR must be sent to the master processing unit for de-interleaving and decoding. This has a cost due to the bandwidth required to exchange data with the master processing unit. Accordingly, as the symbol size and rate increases, the bandwidth requirements also increase. Minimising this bandwidth usage is therefore desirable.
This disclosure aims to help at least some of the above identified concerns by aiming to reduce the memory cost of (de-)interleaver memory for both a conventional and/or a distributed receiver and also for a distributed receiver to reduce the bandwidth cost for the exchange of data, e.g., LLRs. As noted above, the memory cost is related to the size of interleaver required to store the data, whilst reducing bandwidth costs is related to the symbol size (constellation) and the symbol distribution rate. For both scenarios, an efficient representation of the data, such as LLRs, is desirable.
This disclosure represents the data, such as LLRs, in a more efficient way than conventional techniques so that the memory cost or transmission cost of this data is reduced. In some cases this may lead to reduced cost on memory or reduced bandwidth requirement when sending the data, such as to another system for channel decoding.